Long Term Care Data and Policy Peter Kemper Pennsylvania State University April 22, 2002 Board of Scientific Counselors National Center for Health Statistics

Preview of Conclusions 1. Data makes important contributions to policy 2. Its contribution often is not recognized 3. Funding LTC data faces special barriers 4. LTC data needs differ from acute care

1. Data Makes Important Contributions to Policy

Understanding Problems Creates awareness of LTC problems Re-focuses attention on known problems Provides trend data for monitoring programs Examples –Future demand for LTC (Pepper Commission) –Nursing home quality problems (GAO) –Role of family care (AHRQ)

Analysis of Behavior Analysis of behavior Predictors of outcomes of interest E.g., predictors of nursing home use –Targeting home care –Certification of eligibility for nursing home care –Risk adjustment for capitation payments

Predicting Policy Effects: Simulation Models Developed to estimate effect of policy changes –Eligible population –Government costs –Who gains and who loses Brookings/ICF/Lewin LTC simulation model –Consortium of foundations supported in mid-80’s –LTC budget estimates in Clinton health reform –Subsequent updating by ASPE

2. The Contribution of Data Often Is Not Recognized

Route to Policy Is Indirect Other factors Information Constituencies Intermediaries Conventional wisdom Policy makers

Data Investments Must Be Made Far in Advance of Policy Need Data Infrastructure Analysis of Behavior Simulation model Cost estimates

3. Funding LTC Data: Special Barriers Absence of dramatic problems and solutions Enormity of the LTC problem Complexity of the LTC problem Jurisdictional fragmentation Locus of LTC policy in the states

4. LTC Data Needs Differ Support not care—e.g., informal care Data across settings—person-centered data Care transitions—longitudinal data Disability is relatively rare—over-samples Financial resources, household important Adaptability to changing world Private LTC insurance Assisted living Consumer-directed care

Recap 1. Data makes important contributions to policy 2. Its contribution often is not recognized 3. Funding LTC data faces special barriers 4. LTC data needs differ from acute care